A Computational Future for Preventing HIV in Minority Communities: How Advanced Technology Can Improve Implementation of Effective Programs

• Authors: C Hendricks Brown, University of Miami; David C. Mohr, Northwestern University; Carlos G. Gallo, University of Miami; Christopher Mader, University of Miami; Lawrence Palinkas, University of Southern California; Gina Wingood, Emory University; Guillermo Prado, University of Miami; Sheppard G. Kellam, Johns Hopkins University; Hilda Pantin, University of Miami; Jeanne Poduska, American Institutes for Research; Robert Gibbons, University of Chicago; John McManus, University of Miami; Mitsunori Ogihara, University of Miami; Thomas Valente, University of Southern California; Fred Wulczyn, University of Chicago; Sara Czaja, University of Miami; Geoff Sutcliffe, University of Miami; Juan Villamar, University of Miami; Christopher Jacobs, University of Miami
• Journal Title: Journal of Acquired Immune Deficiency Syndromes
• Volume: Volume 63, Number SUPPL. 1
• Publisher: Lippincott, Williams & Wilkins | 2013-06-01, Pages S72-S84
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1097/QAI.0b013e31829372bd
• Subjects: Health Sciences, Public Health; Health Sciences, Medicine and Surgery; Health Sciences, Education
• Keywords: Science & Technology; Life Sciences & Biomedicine; Immunology; Infectious Diseases; implementation science; systems science; behavioral intervention technology; machine learning; computational linguistics; timecast; scientific equity; RISK-REDUCTION INTERVENTION; RANDOMIZED CONTROLLED-TRIAL; HIV/STI BEHAVIORAL INTERVENTIONS; SEXUALLY-TRANSMITTED-DISEASE; AGENT-BASED MODELS; UNITED-STATES; PUBLIC-HEALTH; MENTAL-HEALTH; DISRUPTIVE BEHAVIOR; AGGRESSIVE-BEHAVIOR

Abstract

African Americans and Hispanics in the United States have much higher rates of HIV than non-minorities. There is now strong evidence that a range of behavioral interventions are efficacious in reducing sexual risk behavior in these populations. Although a handful of these programs are just beginning to be disseminated widely, we still have not implemented effective programs to a level that would reduce the population incidence of HIV for minorities. We proposed that innovative approaches involving computational technologies be explored for their use in both developing new interventions and in supporting wide-scale implementation of effective behavioral interventions. Mobile technologies have a place in both of these activities. First, mobile technologies can be used in sensing contexts and interacting to the unique preferences and needs of individuals at times where intervention to reduce risk would be most impactful. Second, mobile technologies can be used to improve the delivery of interventions by facilitators and their agencies. Systems science methods including social network analysis, agent-based models, computational linguistics, intelligent data analysis, and systems and software engineering all have strategic roles that can bring about advances in HIV prevention in minority communities. Using an existing mobile technology for depression and 3 effective HIV prevention programs, we illustrated how 8 areas in the intervention/implementation process can use innovative computational approaches to advance intervention adoption, fidelity, and sustainability.